import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import os
import warnings
warnings.filterwarnings('ignore')
print (os.getcwd())
C:\Users\DELL
os.chdir ('C:\\project\\Projects\\PRJ Car Price Prediction')
print (os.getcwd())
C:\project\Projects\PRJ Car Price Prediction
df=pd.read_csv("audi.csv")
display(df)
| model | year | price | transmission | mileage | fuelType | tax | mpg | engineSize | |
|---|---|---|---|---|---|---|---|---|---|
| 0 | A1 | 2017 | 12500 | Manual | 15735 | Petrol | 150 | 55.4 | 1.4 |
| 1 | A6 | 2016 | 16500 | Automatic | 36203 | Diesel | 20 | 64.2 | 2.0 |
| 2 | A1 | 2016 | 11000 | Manual | 29946 | Petrol | 30 | 55.4 | 1.4 |
| 3 | A4 | 2017 | 16800 | Automatic | 25952 | Diesel | 145 | 67.3 | 2.0 |
| 4 | A3 | 2019 | 17300 | Manual | 1998 | Petrol | 145 | 49.6 | 1.0 |
| ... | ... | ... | ... | ... | ... | ... | ... | ... | ... |
| 10663 | A3 | 2020 | 16999 | Manual | 4018 | Petrol | 145 | 49.6 | 1.0 |
| 10664 | A3 | 2020 | 16999 | Manual | 1978 | Petrol | 150 | 49.6 | 1.0 |
| 10665 | A3 | 2020 | 17199 | Manual | 609 | Petrol | 150 | 49.6 | 1.0 |
| 10666 | Q3 | 2017 | 19499 | Automatic | 8646 | Petrol | 150 | 47.9 | 1.4 |
| 10667 | Q3 | 2016 | 15999 | Manual | 11855 | Petrol | 150 | 47.9 | 1.4 |
10668 rows × 9 columns
import pandas_profiling as pf
display(pf.ProfileReport(df))
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import pandas_profiling as pf
display(pf.ProfileReport(df))
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print (len(df))
10668
display (df.shape)
(10668, 9)
display (df.dtypes )
model object year int64 price int64 transmission object mileage int64 fuelType object tax int64 mpg float64 engineSize float64 dtype: object
display (df.isna().sum() )
model 0 year 0 price 0 transmission 0 mileage 0 fuelType 0 tax 0 mpg 0 engineSize 0 dtype: int64
print (df.info())
<class 'pandas.core.frame.DataFrame'> RangeIndex: 10668 entries, 0 to 10667 Data columns (total 9 columns): # Column Non-Null Count Dtype --- ------ -------------- ----- 0 model 10668 non-null object 1 year 10668 non-null int64 2 price 10668 non-null int64 3 transmission 10668 non-null object 4 mileage 10668 non-null int64 5 fuelType 10668 non-null object 6 tax 10668 non-null int64 7 mpg 10668 non-null float64 8 engineSize 10668 non-null float64 dtypes: float64(2), int64(4), object(3) memory usage: 750.2+ KB None
display (df.describe ())
| year | price | mileage | tax | mpg | engineSize | |
|---|---|---|---|---|---|---|
| count | 10668.000000 | 10668.000000 | 10668.000000 | 10668.000000 | 10668.000000 | 10668.000000 |
| mean | 2017.100675 | 22896.685039 | 24827.244001 | 126.011436 | 50.770022 | 1.930709 |
| std | 2.167494 | 11714.841888 | 23505.257205 | 67.170294 | 12.949782 | 0.602957 |
| min | 1997.000000 | 1490.000000 | 1.000000 | 0.000000 | 18.900000 | 0.000000 |
| 25% | 2016.000000 | 15130.750000 | 5968.750000 | 125.000000 | 40.900000 | 1.500000 |
| 50% | 2017.000000 | 20200.000000 | 19000.000000 | 145.000000 | 49.600000 | 2.000000 |
| 75% | 2019.000000 | 27990.000000 | 36464.500000 | 145.000000 | 58.900000 | 2.000000 |
| max | 2020.000000 | 145000.000000 | 323000.000000 | 580.000000 | 188.300000 | 6.300000 |
X = df.iloc[:,[0,1,3,4,5,6,7,8]].values
display (X.shape)
display (X)
(10668, 8)
array([[' A1', 2017, 'Manual', ..., 150, 55.4, 1.4],
[' A6', 2016, 'Automatic', ..., 20, 64.2, 2.0],
[' A1', 2016, 'Manual', ..., 30, 55.4, 1.4],
...,
[' A3', 2020, 'Manual', ..., 150, 49.6, 1.0],
[' Q3', 2017, 'Automatic', ..., 150, 47.9, 1.4],
[' Q3', 2016, 'Manual', ..., 150, 47.9, 1.4]], dtype=object)
Y = df.iloc[:,[2]].values
display (Y.shape)
display (Y)
(10668, 1)
array([[12500],
[16500],
[11000],
...,
[17199],
[19499],
[15999]], dtype=int64)
display(pd.DataFrame(X).head(5))
| 0 | 1 | 2 | 3 | 4 | 5 | 6 | 7 | |
|---|---|---|---|---|---|---|---|---|
| 0 | A1 | 2017 | Manual | 15735 | Petrol | 150 | 55.4 | 1.4 |
| 1 | A6 | 2016 | Automatic | 36203 | Diesel | 20 | 64.2 | 2.0 |
| 2 | A1 | 2016 | Manual | 29946 | Petrol | 30 | 55.4 | 1.4 |
| 3 | A4 | 2017 | Automatic | 25952 | Diesel | 145 | 67.3 | 2.0 |
| 4 | A3 | 2019 | Manual | 1998 | Petrol | 145 | 49.6 | 1.0 |
from sklearn.preprocessing import LabelEncoder
le1 = LabelEncoder()
X[:,0] = le1.fit_transform(X[:,0])
le2 = LabelEncoder()
X[:,-4] = le2.fit_transform(X[:,-4])
display (X)
array([[0, 2017, 'Manual', ..., 150, 55.4, 1.4],
[5, 2016, 'Automatic', ..., 20, 64.2, 2.0],
[0, 2016, 'Manual', ..., 30, 55.4, 1.4],
...,
[2, 2020, 'Manual', ..., 150, 49.6, 1.0],
[9, 2017, 'Automatic', ..., 150, 47.9, 1.4],
[9, 2016, 'Manual', ..., 150, 47.9, 1.4]], dtype=object)
from sklearn.preprocessing import OneHotEncoder
from sklearn.compose import ColumnTransformer
ct = ColumnTransformer(transformers = [('encoder',OneHotEncoder(),[2])],remainder='passthrough')
X = ct.fit_transform(X)
display (X.shape)
display (pd.DataFrame(X))
(10668, 10)
| 0 | 1 | 2 | 3 | 4 | 5 | 6 | 7 | 8 | 9 | |
|---|---|---|---|---|---|---|---|---|---|---|
| 0 | 0.0 | 1.0 | 0.0 | 0 | 2017 | 15735 | 2 | 150 | 55.4 | 1.4 |
| 1 | 1.0 | 0.0 | 0.0 | 5 | 2016 | 36203 | 0 | 20 | 64.2 | 2.0 |
| 2 | 0.0 | 1.0 | 0.0 | 0 | 2016 | 29946 | 2 | 30 | 55.4 | 1.4 |
| 3 | 1.0 | 0.0 | 0.0 | 3 | 2017 | 25952 | 0 | 145 | 67.3 | 2.0 |
| 4 | 0.0 | 1.0 | 0.0 | 2 | 2019 | 1998 | 2 | 145 | 49.6 | 1.0 |
| ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... |
| 10663 | 0.0 | 1.0 | 0.0 | 2 | 2020 | 4018 | 2 | 145 | 49.6 | 1.0 |
| 10664 | 0.0 | 1.0 | 0.0 | 2 | 2020 | 1978 | 2 | 150 | 49.6 | 1.0 |
| 10665 | 0.0 | 1.0 | 0.0 | 2 | 2020 | 609 | 2 | 150 | 49.6 | 1.0 |
| 10666 | 1.0 | 0.0 | 0.0 | 9 | 2017 | 8646 | 2 | 150 | 47.9 | 1.4 |
| 10667 | 0.0 | 1.0 | 0.0 | 9 | 2016 | 11855 | 2 | 150 | 47.9 | 1.4 |
10668 rows × 10 columns
display (pd.DataFrame(X))
| 0 | 1 | 2 | 3 | 4 | 5 | 6 | 7 | 8 | 9 | |
|---|---|---|---|---|---|---|---|---|---|---|
| 0 | 0.0 | 1.0 | 0.0 | 0 | 2017 | 15735 | 2 | 150 | 55.4 | 1.4 |
| 1 | 1.0 | 0.0 | 0.0 | 5 | 2016 | 36203 | 0 | 20 | 64.2 | 2.0 |
| 2 | 0.0 | 1.0 | 0.0 | 0 | 2016 | 29946 | 2 | 30 | 55.4 | 1.4 |
| 3 | 1.0 | 0.0 | 0.0 | 3 | 2017 | 25952 | 0 | 145 | 67.3 | 2.0 |
| 4 | 0.0 | 1.0 | 0.0 | 2 | 2019 | 1998 | 2 | 145 | 49.6 | 1.0 |
| ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... |
| 10663 | 0.0 | 1.0 | 0.0 | 2 | 2020 | 4018 | 2 | 145 | 49.6 | 1.0 |
| 10664 | 0.0 | 1.0 | 0.0 | 2 | 2020 | 1978 | 2 | 150 | 49.6 | 1.0 |
| 10665 | 0.0 | 1.0 | 0.0 | 2 | 2020 | 609 | 2 | 150 | 49.6 | 1.0 |
| 10666 | 1.0 | 0.0 | 0.0 | 9 | 2017 | 8646 | 2 | 150 | 47.9 | 1.4 |
| 10667 | 0.0 | 1.0 | 0.0 | 9 | 2016 | 11855 | 2 | 150 | 47.9 | 1.4 |
10668 rows × 10 columns
from sklearn.preprocessing import StandardScaler
sc = StandardScaler()
X = sc.fit_transform(X)
display (pd.DataFrame(X))
| 0 | 1 | 2 | 3 | 4 | 5 | 6 | 7 | 8 | 9 | |
|---|---|---|---|---|---|---|---|---|---|---|
| 0 | -0.583268 | 1.200728 | -0.712333 | -1.123544 | -0.046450 | -0.386836 | 1.050783 | 0.357147 | 0.357550 | -0.880218 |
| 1 | 1.714479 | -0.832828 | -0.712333 | -0.160831 | -0.507834 | 0.483989 | -0.954181 | -1.578323 | 1.037130 | 0.114925 |
| 2 | -0.583268 | 1.200728 | -0.712333 | -1.123544 | -0.507834 | 0.217781 | 1.050783 | -1.429440 | 0.357550 | -0.880218 |
| 3 | 1.714479 | -0.832828 | -0.712333 | -0.545916 | -0.046450 | 0.047853 | -0.954181 | 0.282706 | 1.276528 | 0.114925 |
| 4 | -0.583268 | 1.200728 | -0.712333 | -0.738459 | 0.876318 | -0.971285 | 1.050783 | 0.282706 | -0.090355 | -1.543647 |
| ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... |
| 10663 | -0.583268 | 1.200728 | -0.712333 | -0.738459 | 1.337702 | -0.885343 | 1.050783 | 0.282706 | -0.090355 | -1.543647 |
| 10664 | -0.583268 | 1.200728 | -0.712333 | -0.738459 | 1.337702 | -0.972136 | 1.050783 | 0.357147 | -0.090355 | -1.543647 |
| 10665 | -0.583268 | 1.200728 | -0.712333 | -0.738459 | 1.337702 | -1.030381 | 1.050783 | 0.357147 | -0.090355 | -1.543647 |
| 10666 | 1.714479 | -0.832828 | -0.712333 | 0.609339 | -0.046450 | -0.688442 | 1.050783 | 0.357147 | -0.221637 | -0.880218 |
| 10667 | -0.583268 | 1.200728 | -0.712333 | 0.609339 | -0.507834 | -0.551913 | 1.050783 | 0.357147 | -0.221637 | -0.880218 |
10668 rows × 10 columns
from sklearn.model_selection import train_test_split
(X_train,X_test,Y_train,Y_test) = train_test_split(X,Y,test_size=0.2,random_state=0)
print (X.shape, Y.shape)
print (X_train.shape, Y_train.shape)
print (X_test.shape, Y_test.shape)
(10668, 10) (10668, 1) (8534, 10) (8534, 1) (2134, 10) (2134, 1)
from sklearn.ensemble import RandomForestRegressor
regression = RandomForestRegressor(random_state=0)
regression.fit(X_train,Y_train)
display (regression)
RandomForestRegressor(random_state=0)In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook.
RandomForestRegressor(random_state=0)
y_pred = regression.predict(X_test)
display (y_pred)
array([14337.15, 23450.35, 27330.07, ..., 46275.18, 31359. , 9929.62])
print(np.concatenate((y_pred.reshape(len(y_pred),1),Y_test.reshape(len(Y_test),1)),1))
[[14337.15 14998. ] [23450.35 21950. ] [27330.07 28990. ] ... [46275.18 45995. ] [31359. 30500. ] [ 9929.62 8400. ]]
from sklearn.metrics import r2_score,mean_absolute_error
print ('R2 Score ', r2_score(Y_test, y_pred))
print ('Mean Absolute Error', mean_absolute_error(Y_test,y_pred))
R2 Score 0.9536134841307546 Mean Absolute Error 1538.730980670462
from sklearn.linear_model import LinearRegression
reg = LinearRegression()
reg.fit(X_train,Y_train)
print(reg)
LinearRegression()
y_pred = reg.predict(X_test)
display (y_pred)
array([[13121.75937793],
[29393.75937793],
[31873.75937793],
...,
[42565.75937793],
[31485.75937793],
[ 7377.75937793]])
print(np.concatenate((y_pred.reshape(len(y_pred),1),Y_test.reshape(len(Y_test),1)),1))
[[13121.75937793 14998. ] [29393.75937793 21950. ] [31873.75937793 28990. ] ... [42565.75937793 45995. ] [31485.75937793 30500. ] [ 7377.75937793 8400. ]]
from sklearn.metrics import r2_score,mean_absolute_error
print ('R2 Score ', r2_score(Y_test, y_pred))
print ('Mean Absolute Error', mean_absolute_error(Y_test,y_pred))
R2 Score 0.7915565638111468 Mean Absolute Error 3379.9270826343727
y_pred = reg.predict(X)
display (y_pred)
array([[14681.75937793],
[20633.75937793],
[13881.75937793],
...,
[19449.75937793],
[20965.75937793],
[16737.75937793]])
result = pd.concat([df,pd.DataFrame(y_pred)],axis=1)
display( result)
| model | year | price | transmission | mileage | fuelType | tax | mpg | engineSize | 0 | |
|---|---|---|---|---|---|---|---|---|---|---|
| 0 | A1 | 2017 | 12500 | Manual | 15735 | Petrol | 150 | 55.4 | 1.4 | 14681.759378 |
| 1 | A6 | 2016 | 16500 | Automatic | 36203 | Diesel | 20 | 64.2 | 2.0 | 20633.759378 |
| 2 | A1 | 2016 | 11000 | Manual | 29946 | Petrol | 30 | 55.4 | 1.4 | 13881.759378 |
| 3 | A4 | 2017 | 16800 | Automatic | 25952 | Diesel | 145 | 67.3 | 2.0 | 19885.759378 |
| 4 | A3 | 2019 | 17300 | Manual | 1998 | Petrol | 145 | 49.6 | 1.0 | 17397.759378 |
| ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... |
| 10663 | A3 | 2020 | 16999 | Manual | 4018 | Petrol | 145 | 49.6 | 1.0 | 19237.759378 |
| 10664 | A3 | 2020 | 16999 | Manual | 1978 | Petrol | 150 | 49.6 | 1.0 | 19321.759378 |
| 10665 | A3 | 2020 | 17199 | Manual | 609 | Petrol | 150 | 49.6 | 1.0 | 19449.759378 |
| 10666 | Q3 | 2017 | 19499 | Automatic | 8646 | Petrol | 150 | 47.9 | 1.4 | 20965.759378 |
| 10667 | Q3 | 2016 | 15999 | Manual | 11855 | Petrol | 150 | 47.9 | 1.4 | 16737.759378 |
10668 rows × 10 columns
from sklearn.ensemble import ExtraTreesRegressor
ET_Model=ExtraTreesRegressor(n_estimators = 120)
ET_Model.fit(X_train,Y_train)
y_predict=ET_Model.predict(X_test)
from sklearn.metrics import r2_score,mean_absolute_error
print ('R2 Score ', r2_score(Y_test, y_predict))
print ('Mean Absolute Error', mean_absolute_error(Y_test,y_predict))
R2 Score 0.95530840142209 Mean Absolute Error 1544.252601530772
y_pred = reg.predict(X)
display (y_pred)
result = pd.concat([df,pd.DataFrame(y_pred)],axis=1)
display( result)
array([[14681.75937793],
[20633.75937793],
[13881.75937793],
...,
[19449.75937793],
[20965.75937793],
[16737.75937793]])
| model | year | price | transmission | mileage | fuelType | tax | mpg | engineSize | 0 | |
|---|---|---|---|---|---|---|---|---|---|---|
| 0 | A1 | 2017 | 12500 | Manual | 15735 | Petrol | 150 | 55.4 | 1.4 | 14681.759378 |
| 1 | A6 | 2016 | 16500 | Automatic | 36203 | Diesel | 20 | 64.2 | 2.0 | 20633.759378 |
| 2 | A1 | 2016 | 11000 | Manual | 29946 | Petrol | 30 | 55.4 | 1.4 | 13881.759378 |
| 3 | A4 | 2017 | 16800 | Automatic | 25952 | Diesel | 145 | 67.3 | 2.0 | 19885.759378 |
| 4 | A3 | 2019 | 17300 | Manual | 1998 | Petrol | 145 | 49.6 | 1.0 | 17397.759378 |
| ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... |
| 10663 | A3 | 2020 | 16999 | Manual | 4018 | Petrol | 145 | 49.6 | 1.0 | 19237.759378 |
| 10664 | A3 | 2020 | 16999 | Manual | 1978 | Petrol | 150 | 49.6 | 1.0 | 19321.759378 |
| 10665 | A3 | 2020 | 17199 | Manual | 609 | Petrol | 150 | 49.6 | 1.0 | 19449.759378 |
| 10666 | Q3 | 2017 | 19499 | Automatic | 8646 | Petrol | 150 | 47.9 | 1.4 | 20965.759378 |
| 10667 | Q3 | 2016 | 15999 | Manual | 11855 | Petrol | 150 | 47.9 | 1.4 | 16737.759378 |
10668 rows × 10 columns
from sklearn.model_selection import RandomizedSearchCV
n_estimators = [int(x) for x in np.linspace(start = 80, stop = 1500, num = 10)]
max_features = ['auto', 'sqrt']
max_depth = [int(x) for x in np.linspace(6, 45, num = 5)]
min_samples_split = [2, 5, 10, 15, 100]
min_samples_leaf = [1, 2, 5, 10]
# create random grid
rand_grid={'n_estimators': n_estimators,
'max_features': max_features,
'max_depth': max_depth,
'min_samples_split': min_samples_split,
'min_samples_leaf': min_samples_leaf}
rf=RandomForestRegressor()
rCV=RandomizedSearchCV(estimator=rf,param_distributions=rand_grid,scoring='neg_mean_squared_error',n_iter=3,cv=3,random_state=42, n_jobs = 1)
rf_pred=rCV.predict(X_test)
display (rf_pred)
array([14058.87990831, 23737.34462538, 28473.71003631, ...,
48104.76434693, 31300.69766831, 10037.72500503])
from sklearn.metrics import mean_absolute_error,mean_squared_error
print('MAE',mean_absolute_error(Y_test,rf_pred))
print('MSE',mean_squared_error(Y_test,rf_pred))
MAE 1502.6607191516405 MSE 5695470.401678256
display (r2_score(Y_test,rf_pred))
0.9585530804600272
from catboost import CatBoostRegressor
cat=CatBoostRegressor()
print (cat.fit(X_train,Y_train))
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cat_pred=cat.predict(X_test)
display (cat_pred)
array([13386.63817795, 24056.86842882, 28082.99514751, ...,
45959.31234642, 31714.44751009, 9481.45994163])
display (r2_score(Y_test,cat_pred))
0.9641612028134969
import pickle
# Saving model to disk
pickle.dump(cat, open('model.pkl','wb'))
model=pickle.load(open('model.pkl','rb'))
print (model.predict (X_train))
[14256.69390956 29475.32741129 11911.12888914 ... 21406.16876331 17189.65426694 44839.56117235]